In the development of the Strategic Public Transportation System of Montería it was necessary to make adjustments to three urban intersections. In this situation, a transit study was carried out, and it has allowed us to determine which of the different alternatives proposed to make the adjustments was the most suitable in terms of transit; this analysis was done in advanced with micro simulation tools. This study also explains the methodology to determine the existing transit, using in the first-place secondary information to establish critic periods for which the information of primary vehicular field is taken in advance by capacity. Then, peak hours are determined and by micro modelacion tools it's establish the main management indicators in terms of capacity and level of service that will be useful as a tool to decide the viability of the alternatives in terms of the evaluated transit intersections

This study develops a landslide susceptibility zoning map for the Zipaquirá–Pacho road corridor in Cundinamarca, an area prone to frequent landslides. Two statistical methods—Weight of Evidence (WoE) and Information Value (IV)—were used alongside various causal factors to generate the map using GIS software (ArcGIS Pro 3.5.0 software.). A landslide inventory with 101 points was compiled through fieldwork and Google Earth image analysis. Of these, 70% were used to build the models, while the remaining 30% were reserved for validation, ensuring spatial representativeness. The resulting susceptibility maps classified the area into five categories: “very high”, “high”, “moderate”, “low”, and “very low.” For WoE, 19.62% of the area was classified as “very high” and 19.71% as “high”, while for IV, the respective values were 17.57% and 26.55%. Notably, 88% of the identified landslides occurred in “high” and “very high” zones. Model validation using the AUC (Area Under Curve) metric yielded an efficiency of 81%, confirming the reliability of both methods for landslide prediction. The study’s findings are essential for supporting mitigation strategies and serve as valuable input for local authorities and stakeholders involved in risk management and infrastructure planning.

This study evaluates the use of coffee husk ash (CHA) as a stabilizer for improving the physical and mechanical properties of low-plasticity clay soils. CHA was incorporated at 4%, 6%, and 8% by weight. Laboratory tests included compaction, unconfined compressive strength (UCS) under dry and saturated conditions, California Bearing Ratio (CBR), resilient modulus, matrix suction, and soil expansion. Results showed significant improvements: CBR increased by 356%, soil expansion was reduced by 24%, and UCS improved by 17.4% (dry) and 28.0% (saturated) compared to natural soil. Higher ash content led to greater water retention and lower matrix suction, enhancing internal cohesion and stiffness under varying moisture. The resilient modulus also increased with curing time and ash content, reaching up to 25% after 28 days. These findings demonstrate that CHA can significantly enhance both mechanical and hydromechanical behavior of clay soils, making it a suitable alternative for road and infrastructure applications. One challenge addressed in this study is the lack of standardization in the use of agricultural ashes for soil stabilization. Since coffee husk ash is often discarded or underutilized, its application in geotechnical contexts may offer a promising alternative to reduce waste and promote the use of locally available materials.

This study develops a landslide susceptibility zoning map for the Zipaquirá–Pacho road corridor in Cundinamarca, an area prone to frequent landslides. Two statistical methods—Weight of Evidence (WoE) and Information Value (IV)—were used alongside various causal factors to generate the map using GIS software. A landslide inventory with 101 points was compiled through fieldwork and Google Earth image analysis. Of these, 70% were used to build the models, while the remaining 30% were reserved for validation, ensuring spatial representativeness. The resulting susceptibility maps classified the area into five categories: "very high," "high," "moderate," "low," and "very low." For WoE, 19.62% of the area was classified as "very high" and 19.71% as "high," while for IV, the respective values were 17.57% and 26.55%. Notably, 88% of the identified landslides occurred in "high" and "very high" zones. Model validation using the AUC (Area Under Curve) metric yielded an efficiency of 81%, confirming the reliability of both methods for landslide prediction. The study's findings are essential for supporting mitigation strategies and serve as valuable input for local authorities and stakeholders involved in risk management and infrastructure planning.